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| """
二值接触感知坐姿识别系统
论文核心实现：10个接触开关 → 4种坐姿分类
"""
import numpy as np
from sklearn.tree import DecisionTreeClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score
import json
class BinaryContactPosture:
"""
二值接触感知坐姿识别
硬件：10个机械接触开关（5×2阵列）
信号：10-bit 二进制向量
分类器：决策树 / 逻辑回归
"""
SWITCH_LAYOUT = {
'S1': {'row': 0, 'col': 0}, 'S2': {'row': 0, 'col': 1}, 'S3': {'row': 0, 'col': 2},
'S4': {'row': 1, 'col': 0}, 'S5': {'row': 1, 'col': 1}, 'S6': {'row': 1, 'col': 2},
'S7': {'row': 2, 'col': 0}, 'S8': {'row': 2, 'col': 1}, 'S9': {'row': 2, 'col': 2},
'S10': {'row': 3, 'col': 1}
}
POSTURE_SIGNATURES = {
'normal': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
'leaning_back': [1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
'leaning_left': [1, 0, 0, 1, 0, 0, 1, 0, 0, 0],
'leaning_right': [0, 0, 1, 0, 0, 1, 0, 0, 1, 0],
}
def __init__(self, classifier: str = 'tree'):
if classifier == 'tree':
self.clf = DecisionTreeClassifier(
max_depth=5,
criterion='entropy'
)
elif classifier == 'logistic':
self.clf = LogisticRegression(max_iter=200)
else:
raise ValueError(f"Unknown classifier: {classifier}")
self.is_trained = False
def generate_training_data(self, n_samples: int = 200) -> tuple:
"""生成带噪声的训练数据"""
X, y = [], []
for posture, signature in self.POSTURE_SIGNATURES.items():
for _ in range(n_samples // len(self.POSTURE_SIGNATURES)):
noisy = np.array(signature, dtype=float)
noise_idx = np.random.choice(10, size=np.random.randint(0, 2), replace=False)
noisy[noise_idx] = 1 - noisy[noise_idx]
X.append(noisy)
y.append(posture)
return np.array(X), np.array(y)
def train(self, X: np.ndarray, y: np.ndarray):
"""训练分类器"""
self.clf.fit(X, y)
self.is_trained = True
scores = cross_val_score(self.clf, X, y, cv=5)
print(f"训练完成 - 5折交叉验证准确率: {scores.mean():.1%} ± {scores.std():.1%}")
return scores
def predict(self, switch_states: list) -> str:
"""
实时预测坐姿
Args:
switch_states: 10个开关状态 (0/1)
Returns:
posture: 坐姿类别
"""
if not self.is_trained:
raise RuntimeError("模型未训练")
X = np.array(switch_states).reshape(1, -1)
return self.clf.predict(X)[0]
def shap_analysis(self, X: np.ndarray) -> dict:
"""
SHAP 分析：哪些开关最重要？
论文发现：中央接触点（S2, S5, S8）最有信息量
"""
if hasattr(self.clf, 'feature_importances_'):
importances = self.clf.feature_importances_
else:
importances = np.abs(self.clf.coef_[0])
switch_names = list(self.SWITCH_LAYOUT.keys())
sorted_idx = np.argsort(importances)[::-1]
result = {}
for idx in sorted_idx:
result[switch_names[idx]] = float(importances[idx])
return result
if __name__ == "__main__":
np.random.seed(42)
system = BinaryContactPosture(classifier='tree')
X, y = system.generate_training_data(200)
print(f"训练数据: {X.shape}")
print(f"类别: {np.unique(y)}")
scores = system.train(X, y)
test_cases = [
([1, 1, 1, 1, 1, 1, 1, 1, 1, 1], "正常坐姿"),
([1, 1, 1, 0, 0, 0, 0, 0, 0, 0], "后仰"),
([1, 0, 0, 1, 0, 0, 1, 0, 0, 0], "左倾"),
([0, 0, 1, 0, 0, 1, 0, 0, 1, 0], "右倾"),
]
print("\n" + "=" * 50)
print("坐姿预测测试")
print("=" * 50)
for switches, expected in test_cases:
prediction = system.predict(switches)
print(f" 开关: {switches}")
print(f" 预期: {expected}")
print(f" 预测: {prediction}")
print()
print("=" * 50)
print("特征重要性分析（SHAP近似）")
print("=" * 50)
importances = system.shap_analysis(X)
for name, imp in importances.items():
pos = system.SWITCH_LAYOUT[name]
print(f" {name} (行{pos['row']},列{pos['col']}): {imp:.4f}")
|
